Dhall, A. — Page 3 | Search bioRxiv

bioRxiv2018

PCIF1 catalyzes m6Am mRNA methylation to regulate gene expression

mRNA modifications play an important role in regulating gene expression. One of the most abundant mRNA modifications is N6,2-O-dimethyladenosine (m6Am). Here, we demonstrate that m6Am is an evolutionarily conserved mRNA modification mediated by the Phosphorylated CTD Interacting Factor 1 (PCIF1), which catalyzes m6A methylation on 2-O-methylated adenine located at the 5 ends of mRNAs. Furthermore, PCIF1 catalyzes only 5 m6Am methylation of capped mRNAs, but not internal m6A methylation in vitro and in vivo. Our global mRNA methylation analysis revealed that there is no crosstalk between m6Am and m6A mRNA methylation events, suggesting that m6Am is functionally distinct from m6A. Importantly, our data indicate that m6Am negatively impacts translation of methylated mRNAs by antagonizing cap binding protein eIF4E. Together, we identify the first and only human mRNA m6Am methyltransferase and demonstrate a novel mechanism of gene expression regulation through PCIF1-mediated m6Am mRNA methylation in eukaryotes.\n\nHighlightsO_LIPCIF1 is an evolutionarily conserved mRNA m6Am methyltransferase\nC_LIO_LILoss of PCIF1 leads to a complete loss of m6Am, whereas m6A level and distribution are not affected\nC_LIO_LIPCIF1 mediated m6Am does not affect RNA Pol II transcription or mRNA stability\nC_LIO_LIm6Am-Exo-Seq is a robust methodology that enables global m6Am mapping\nC_LIO_LIm6Am suppresses cap dependent translation\nC_LI

molecular biology↗

bioRxiv2018

Prediction and analysis of skin cancer progression using genomics profiles of patients

Metastatic state of the Skin Cutaneous Melanoma (SKCM) has led to high mortality rate worldwide. Previously, various studies have revealed the association of the metastatic melanoma with the diminished survival rate in comparison to primary tumors. Thus, prediction of melanoma at primary tumor state is crucial to employ optimal therapeutic strategy for prolonged survival of patients. The RNA, miRNA and methylation data of The Cancer Genome Atlas (TCGA) cohort of SKCM is comprehensively analysed to recognize key genomic features that can categorize various states of metastatic tumors from primary tumors with high precision. Subsequently, various prediction models were developed using filtered genomic features implementing various machine learning techniques to classify these primary tumors from metastatic tumors. The SVC model (with class weight and RBF kernel) developed using 17 mRNA features achieved maximum MCC 0.73 with sensitivity, specificity and accuracy 89.19%, 90.48% and 89.47% respectively on independent validation dataset. Our study reveals that gene expression based features performs better than features obtained from miRNA profiling and epigenomic profiling. Our analysis shows that the expression of genes C7, MMP3, KRT14, KRT17, MASP1, and miRNA hsa-mir-205 and hsa-mir-203a are among the key genomic features that may substantially contribute to the oncogenesis of melanoma even on the basis of simple expression threshold. The major prediction models and analysis modules to predict metastatic and primary tumor samples of SKCM are available from a webserver, CancerSPP (http://webs.iiitd.edu.in/raghava/cancerspp/).

bioinformatics↗